ISCAP Proceedings: Abstract Presentation
AI-Augmented Interdisciplinarity in Undergraduate Business Education: Rethinking the Breadth–Depth Tradeoff
Yi Sun
California State University - San Marcos
Ronald Ramirez
California State University - San Marcos
Abstract
Artificial intelligence is changing both how business is conducted and the capabilities business graduates will need for future careers. Machine learning and generative AI is creating a new resource for skill and labor, enabling digital capabilities for prediction, analysis, information processing, and other specialized expertise. This transformation of work raises a business curriculum question that goes beyond simply adding AI to existing courses or AI courses to existing programs. AI is fundamentally changing how to be a successful business employee and how to prepare for a business career. For higher education, it raises a central question: How does AI change traditional business education design? We investigate this question by examining the trade-off between disciplinary depth and interdisciplinary breadth in undergraduate business education.
Interdisciplinary business education is not new. Its traditional challenge is balancing the need to provide students with a breadth of knowledge across the subjects of finance, accounting, marketing, operations, MIS, analytics, and management broadly, with a depth of knowledge in a student’s choice of specialty. AI does not eliminate this tradeoff. However, the new technology resource is reshaping what is required of new business graduates and is changing what knowledge must be acquired through a business degree. Especially in a future where AI investments enable the creation of an individually empowered, cross functional workforce.
This paper presents an AI-integrated undergraduate business option, scheduled to launch in Fall 2027, as a curriculum innovation case. The model combines a base layer of functional fluency across business disciplines, with AI-enabled on-demand depth of specialized knowledge, AI-assisted cross-functional integration, and human-centric decision ownership. Students develop sufficient disciplinary knowledge to frame problems, understand assumptions, evaluate AI-supported analyses, critically assess implications, and work with a team to make impactful decisions.
This approach also changes the focus of analytical skills. As AI increasingly assists with computation, modeling, and prediction, analytical competence can place greater emphasis on problem formulation, validation, interpretation, integration, and decision-making. An AI-generated demand forecast, for example, may affect pricing, revenue, cash flow, inventory, capacity, staffing, technology, and risk. Similarly, AI may construct a financial model for a proposed capital investment, but deciding whether to invest requires consideration of customer demand, competitive strategy, financing, operational capacity, technology, workforce implications, and risk. AI can assist with specialized analysis; the business decision still requires cross-functional knowledge and human judgment.
Compared with a pre-AI interdisciplinary program, the proposed model adds two capabilities. First, AI may provide on-demand analytical depth, allowing students with sufficient foundations to reach further into specialized problems while recognizing the limits of their expertise. Second, AI may make cross-functional integration faster and more practical by helping students examine how decisions in one business area affect others.
The intended graduate is not a substitute for a business-domain specialist. Rather, they will become an AI-enabled business integrator combining functional fluency, AI-augmented analytical reach, cross-functional judgment, and decision ownership. Students will also learn to identify AI-enabled business opportunities arising from AI’s innovation process benefits.
Short Bibliography
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Information Economics and Policy, 47, 1–6. https://doi.org/10.1016/j.infoecopol.2019.05.001
Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
OECD. (2026). Artificial intelligence and the future of skills. OECD Centre for Educational Research and Innovation. https://www.oecd.org/en/about/projects/artificial-intelligence-and-future-of-skills.html
Sollosy, M., & McInerney, M. (2022). Artificial intelligence and business education: What should be taught. The International Journal of Management Education, 20(3), 100720. https://doi.org/10.1016/j.ijme.2022.100720
Xu, J. J., & Babaian, T. (2021). Artificial intelligence in business curriculum: The pedagogy and learning outcomes. The International Journal of Management Education, 19(3), 100550. https://doi.org/10.1016/j.ijme.2021.100550